MATH20260 The Mathematics of Google

Academic Year 2020/2021

The module is designed to provide an introduction to applied linear algebra with an emphasis on topics used in large data analysis, and in particualar in Google search engine.

The Google search engine has made accessing information easy and its speed and efficacy amazes people. This module explains how it works. An essential component is the ability to rank information according to its importance, and in Google, this relies on simple algebraic principles. The mathematical areas involved are linear algebra and matrix analysis.

The presentation will in part be based on the book by Amy Langville and Carl Meyer "Google's PageRank and Beyond: The Science of Search Engine Rankings" (Princeton University Press 2006)

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Curricular information is subject to change

Learning Outcomes:

Ability to encode large amounts of information using matrices and then efficiently extracting important parts using linear algebra. Understanding stochastic matrices and how to apply their properties in ranking data.

Indicative Module Content:

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Autonomous Student Learning

70

Online Learning

12

Total

106

Approaches to Teaching and Learning:
Lectures, Online Tutorials, Enquiry and Problem-Based Learning. 
Requirements, Exclusions and Recommendations
Learning Requirements:

A knowledge of basic linear algebra (covered in first level linear algebra modules) is needed.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Final examination 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

70

Continuous Assessment: Varies over the semester Unspecified n/a Standard conversion grade scale 40% No

30


Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

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